JP5454466B2 - Character detection device, character detection method, and computer program - Google Patents

Description

  The present invention relates to an apparatus and method for performing image processing on an image including a transparent image.

  In recent years, image forming apparatuses having various functions such as copying, PC printing, scanning, faxing, and file servers have become widespread. Such an image forming apparatus is called a “multifunction machine” or “MFP (Multi Function Peripherals)”.

  PC printing is a function for receiving image data from a personal computer and printing the image on paper.

  In recent years, applications for drawing on personal computers have been distributed. Such an application is called “drawing software”. Some drawing software has a function of displaying a transparent image on a display.

  The “transparent image” has a property that even if there is an image of another object behind, the image of the other object can be seen through.

  That is, for example, as shown in FIG. 4A, the transparent image 40a is superimposed on the left half of the background image 40b. Then, as shown in FIG. 4B, the portion of the background image 40b that overlaps the transparent image 40a is seen through. However, even if a non-transparent image 40c that is not a transparent image is superimposed on the right half of the back image 40b, it cannot be seen through. The higher the transmittance of the transparent image, the more transparent the background image on which it is superimposed.

  The image forming apparatus can print the transparent image displayed on the personal computer on paper. Before printing, the transparent image is subjected to pixel thinning processing as shown in FIGS. 5B and 5C in accordance with the high transmittance. Then, an image behind the transparent image is printed at the thinned pixel position. As a result, the background image appears to show through.

  A technique for detecting characters such as letters or numbers in an image has been put into practical use. Furthermore, a method for accurately detecting characters has been proposed. For example, the following method has been proposed.

  A digital image is divided into a plurality of blocks, a contrast amount relating to pixel values of a plurality of pixels included in the block is obtained, a pixel value bimodal evaluation value relating to a histogram of pixel values of a plurality of pixels included in the block is obtained, and a plurality of A contrast threshold value based on the contrast amount is obtained, a bimodal threshold value is obtained based on a plurality of the pixel value bimodal evaluation values, and the block is classified as a text block or a non-text block. In the classification, the block in which the contrast amount and the pixel value bimodal evaluation value satisfy the first criterion based on the contrast threshold and the bimodal threshold is classified as a text block, and the first The above blocks that do not satisfy one standard are classified as non-text blocks (Patent Document 1).

JP 2010-81604 A

  However, with the conventional method described in Patent Document 1, it is not possible to successfully detect a character on which transparent images are superimposed. This is because it is determined that the entire surface of the portion where the transparent image is superimposed is a text region.

  In view of such a problem, an object of the present invention is to detect a character on which a transparent image is superimposed with higher accuracy than in the past.

A character detection device according to an aspect of the present invention is a character detection device that detects a character from an image including a first image representing a character and a second image representing a translucent object, A frequency distribution calculating means for calculating a frequency of appearance for each gradation of a pixel attribute for each block obtained by dividing an overlapping area, which is an area where the second image overlaps an image, and the frequency A first frequency that is a frequency in a first gradation of the gradations, a second frequency that is a frequency in a second gradation of the gradations, and a first frequency of the gradations The third frequency which is the frequency in the third gradation is a peak, and the difference between the third frequencies in the respective blocks is the difference between the first frequencies in the respective blocks and the respective blocks. The difference between the second frequencies Is larger, a first replacement image is generated by changing the gradation of the pixel of the third gradation among the pixels of the overlapping area to the first gradation, and the pixel of the overlapping area A replacement image generating means for generating a second replacement image by changing the gradation of the pixel of the third gradation to the second gradation, and the first of the first replacement image A first closing processing unit that closes a second gradation pixel; a second closing processing unit that closes the first gradation pixel of the second replacement image; and a pixel in the overlapping region. A set of pixels at the same position as the first gradation pixel of the first replacement image after closing or at the same position as the second gradation pixel of the second replacement image after closing and detected as the character, the character detecting means It has a.

  When the second image is a color image, the attribute is, for example, color brightness. Alternatively, when the first image and the second image are monochrome images, the attribute is, for example, density.

  According to the present invention, it is possible to detect a character on which a transparent image is superimposed with higher accuracy than in the past.

1 is a diagram illustrating an example of a network system including an image forming apparatus. 2 is a diagram illustrating an example of a hardware configuration of an image forming apparatus. FIG. It is a figure which shows the example of a structure of an image processing circuit. It is a figure for demonstrating the example of the superimposition of the transparent image and non-transparent image to a back image. It is a figure for demonstrating the example of the characteristic of a transparent image. It is a figure for demonstrating the example of the superimposition of the transparent image on a back image. It is a figure which shows the example of the positional relationship of the transparent image superimposition area | region at the time of superimposing a transparent image on a back image, and a transparent image non-superimposition area | region. It is a figure which shows the example of the pixel which comprises a transparent image superimposition area | region. It is a histogram which shows the example of distribution of the number of pixels of each brightness. It is a figure which shows the example of a structure of a character pixel discrimination | determination part. It is a figure for demonstrating the example of a process of a 1st pixel replacement part, a 1st closing process part, and a 1st character pixel discrimination | determination part. It is a figure for demonstrating the example of a process of a 2nd pixel replacement part, a 2nd closing process part, and a 2nd character pixel discrimination | determination part. It is a figure for demonstrating the example of a process of an OR operation part.

  FIG. 1 is a diagram illustrating an example of a network system including an image forming apparatus 1. FIG. 2 is a diagram illustrating an example of a hardware configuration of the image forming apparatus 1.

  An image forming apparatus 1 shown in FIG. 1 is an apparatus generally called a multi-function peripheral or MFP (Multi Function Peripherals), and is an apparatus in which functions such as copying, network printing (PC printing), fax, and scanner are integrated. .

  The image forming apparatus 1 can exchange image data with a device such as a personal computer 4A via a communication line 4T such as a LAN (Local Area Network), a public line, or the Internet.

  As shown in FIG. 2, the image forming apparatus 1 includes a CPU (Central Processing Unit) 10a, a RAM (Random Access Memory) 10b, a ROM (Read Only Memory) 10c, a mass storage device 10d, a scanner 10e, a printing device 10f, The network interface 10g, the touch panel display 10h, the modem 10i, and the image processing circuit 10j are configured.

  The scanner 10e is an apparatus that reads an image such as a photograph, a character, a picture, a chart, or the like written on a document sheet and generates image data.

  The touch panel display 10h displays a screen for giving a message or an instruction to the user, a screen for the user to input processing commands and conditions, a screen showing the processing result of the CPU 10a, and the like. Moreover, the position which the user touched with the finger | toe is detected, and the signal which shows a detection result is transmitted to CPU10a.

  The network interface 10g is a NIC (Network Interface Card) for communicating with other devices such as the personal computer 4A via the communication line 4T.

  The modem 10i is a device for exchanging image data with other fax terminals using a protocol such as G3 via a fixed telephone network.

  The image processing circuit 10j performs image processing on the image to be printed based on the image data transmitted from the personal computer 4A. Each unit of the image processing circuit 10j is realized by a circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The processing of each part of the image processing circuit 10j will be described later.

  The printing device 10f prints an image read by the scanner 10e or an image subjected to image processing by the image processing circuit 10j on a sheet.

  The ROM 10c and the large-capacity storage device 10d store programs such as firmware and applications in addition to the OS (Operating System). These programs are loaded into the RAM 10b as necessary and executed by the CPU 10a. A hard disk or a flash memory is used as the large capacity storage device 10d.

  Next, the configuration of the image processing circuit 10j and the image processing by the image processing circuit 10j will be described.

  FIG. 3 is a diagram illustrating an example of the configuration of the image processing circuit 10j. FIG. 4 is a diagram for explaining an example of superimposing the transparent image 40a and the non-transparent image 40c on the background image 40b. FIG. 5 is a diagram for explaining an example of characteristics of a transparent image. FIG. 6 is a diagram for explaining an example of superimposing the transparent image 41a on the background image 41b. FIG. 7 is a diagram illustrating an example of a positional relationship between the transparent image superimposed region 50K and the transparent image non-superimposed region 50L when the transparent image 50a is superimposed on the background image 50b. FIG. 8 is a diagram illustrating an example of pixels constituting the transparent image overlapping region 50K. FIG. 9 is a histogram showing an example of the distribution of the number of pixels of each brightness. FIG. 10 is a diagram illustrating an example of the configuration of the character pixel determination unit 104. FIG. 11 is a diagram for explaining an example of processing of the first pixel replacement unit 303, the first closing processing unit 304, and the first character pixel determination unit 305. FIG. 12 is a diagram for describing an example of processing of the second pixel replacement unit 306, the second closing processing unit 307, and the second character pixel determination unit 308. FIG. 13 is a diagram for explaining an example of processing of the logical sum operation unit 309.

  As shown in FIG. 3, the image processing circuit 10j includes a transparent image superimposition area extraction unit 101, a block division unit 102, a histogram calculation unit 103, a character pixel determination unit 104, a transparent image superimposition area correction unit 105, and the like. . The image processing circuit 10j performs image processing on an image to be printed. Specifically, the image processing is information processing for editing image data 70 representing an image to be printed.

  In the present embodiment, as the image data 70, image data representing a state in which the transparent image is overlapped with another image is used.

  In general, a “transparent image” is an image having a property that an image of another object can be seen through even if there is an image of another object behind. That is, it can be said that the transmission image represents a translucent object such as glass and cellophane.

  For example, as shown in FIG. 4A, the transparent image 40a is overlaid on the left half of the background image 40b, and the non-transparent image 40c is overlaid on the right half of the background image 40b. Then, as shown in FIG. 4B, the portion of the background image 40b that overlaps the transparent image 40a is seen through. However, the portion of the background image 40b that overlaps the non-transparent image 40c does not appear at all.

  The higher the transmittance of the transparent image, the more transparent the other image (that is, the background image) on which the transparent image is superimposed.

  In general, when a transparent image is displayed on a personal computer 4A or the like, as shown in FIG. 5A, even if all pixels have a constant density, FIG. 5B or FIG. As shown in FIG. 5C, conversion is performed so as to be constituted by pixels having a certain density and pixels not having the density.

  Note that in FIGS. 5B and 5C, hatched pixels are pixels having a certain density. On the other hand, pixels that are not hatched are pixels that do not have a certain density. The same applies to FIGS. 6, 8, 11 </ b> A to 11 </ b> C, and FIGS. 12A to 12 </ b> C. Hereinafter, a pixel having a constant density is referred to as a “density pixel”, and a pixel having no constant density is referred to as a “non-density pixel”. “Density” is the gradation of each color (for example, Red, Green, and Blue, respectively) when the transparent image is a color image, and is gray scale when the transparent image is a monochrome image.

  The pixels with density are printed at a determined density. On the other hand, the non-density pixel is not printed if there is no other image behind, but if there is another image, the pixel at the same position as the non-density pixel in the other image is printed.

  Therefore, for example, as shown in FIG. 6, when a part of the transparent image 41 a overlaps a part of the background image 41 b, the corresponding pixel of the background image 41 b is arranged at a non-density pixel position of the transparent image 41 a. By printing, both images are printed as if the back image 41b is seen through the transparent image 41a.

  In addition, the higher the transmittance of the transmission image, the lower the frequency with which the pixels with density appear. Therefore, the transmittance of the transmission image shown in FIG. 5B is higher than that of the transmission image shown in FIG.

  Non-density pixels exist on the top, bottom, left, and right of the pixels with density shown in FIG. On the other hand, pixels with density exist on the top, bottom, left, and right of the non-density pixel shown in FIG.

  Hereinafter, a pixel in which the other type of pixel exists vertically and horizontally is referred to as an “isolated point”. Therefore, in FIG. 5B, a pixel with density is an isolated point pixel, and in FIG. 5C, a pixel without density is an isolated point pixel.

  In the present embodiment, image processing by the image processing circuit 10j will be described by taking as an example a case where image data representing the original image 50 is handled as the image data 70.

  As shown in FIG. 7A, the original image 50 is obtained by superimposing a transparent image 50a on a background image 50b. The user creates the original image 50 using an application such as drawing software installed in the personal computer 4A. As a result, data for reproducing the original image 50 is generated as the image data 70.

  The transparent image 50a is smaller than the background image 50b. Therefore, as shown in FIG. 7B, the original image 50 includes an area where the background image 50b and the transparent image 50a overlap and an area only of the background image 50b. Hereinafter, the former is referred to as “transparent image superimposing region 50K”, and the latter is referred to as “transparent image non-superimposing region 50L”. In addition, a character “A” is written in a portion of the background image 50b where the transparent image 50a overlaps. The color of this character is one specific color (for example, blue). The background color of this character is another specific one color (for example, yellow).

  The personal computer 4A transmits the image data 70 to the image forming apparatus 1 together with a print command.

  When the image forming apparatus 1 receives the print command and the image data 70, each unit of the image processing circuit 10j executes the following processing.

  The transparent image superimposed region extraction unit 101 determines and extracts the transparent image superimposed region 50K from the original image 50.

  Specifically, the transparent image overlapping region extraction unit 101 determines and detects the transparent image overlapping region 50K, for example, as follows based on the above-described characteristics of the transparent image.

  The transparent image superimposed region extraction unit 101 detects isolated points from the original image 50 as follows. Pay attention to one pixel. Hereinafter, this pixel is referred to as a “target pixel”. The density (gradation) of the target pixel is compared with the densities of other pixels (hereinafter referred to as “adjacent pixels”) adjacent to the target pixel in the upper, lower, left, and right directions.

  When the requirement that the difference between the density of the pixel of interest and the density of each of the other pixels is all equal to or greater than a predetermined value β, the transparent image overlapping area extraction unit 101 detects the pixel of interest as an isolated point.

  Note that, when the original image 50 is a color image, the transparent image overlapping region extraction unit 101 performs this comparison independently for each color. If any one of the requirements is satisfied, the target pixel is detected as an isolated point. The same applies to the determination of whether or not the original image 50 is a color image.

  As shown in FIGS. 5B and 5C, the appearance of isolated points in the transmission image has a certain periodicity (regularity). Therefore, the transparent image overlapping region extraction unit 101 extracts a plurality of isolated points having periodicity in appearance in the detected isolated points.

  Then, the transparent image superimposed region extraction unit 101 performs a closing process on an image representing the distribution of the extracted plurality of isolated points (hereinafter referred to as “distribution image”). That is, a process of expanding (expanding) and reducing (contracting) the dot at the position of each isolated point is performed. The position and shape of the distribution image subjected to the closing process substantially correspond to the position and shape of the transmission image superimposing region 50K.

  In this way, the transparent image overlapping area extraction unit 101 identifies the position and shape of the transparent image overlapping area 50K, and extracts the transparent image overlapping area 50K from the original image 50.

  Note that when the transmittance of the transparent image is around 50%, the pixels with density are detected as isolated points, and the pixels of the background image appearing at the positions without density pixels are also detected as isolated points. That is, most pixels in the area are detected as isolated points. The density of each pixel with density is constant, but the pixel of the background image that appears at the position of each pixel without density is not constant. Therefore, when most of the pixels in the region are detected as isolated points, the transparent image overlapping region extraction unit 101 selects only isolated points having a certain density, and displays an image representing the distribution of the selected isolated points. What is necessary is just to perform closing as a distribution image.

  The block dividing unit 102 divides the transparent image superimposed region 50K extracted by the transparent image superimposed region extracting unit 101 into a predetermined number of blocks 51. In the present embodiment, the transparent image overlapping region 50K shown in FIG. 8A is divided into 4 × 4 blocks 51A to 51P as shown in FIG. 8B. The sizes of the blocks 51A to 51P are all equal.

  In FIG. 8A and FIG. 8B, the hatched pixels are the pixels with density of the transmission image 50a. Both the black pixel and the gray pixel are pixels of the background image 50b that appear at the non-density pixel position of the transmission image 50a, but the black pixel is a pixel of the character “A” and is gray These are the pixels of the background of the character.

  The histogram calculation unit 103 calculates a frequency distribution in which the number of pixels of each brightness of each of the blocks 51A to 51P is a frequency. Each calculated frequency distribution can be represented as a histogram as shown in FIG.

  The histograms shown in FIG. 9A, FIG. 9B, and FIG. 9C are histograms representing the frequency distributions of the block 51A, the block 51B, and the block 51C, respectively.

  In these three histograms, two or three peaks appear. Each peak has a distribution of the number of pixels having the same brightness as the pixels having density in the transmission image 50a, a distribution of the number of pixels having the same brightness as the characters in the background image 50b, and the background and brightness of the characters in the background image 50b. Corresponds to any one of the distributions of the number of pixels having the same.

  As shown in FIG. 10, the character pixel determination unit 104 includes 24 comparison operation units, a pixel type lightness determination unit 302, a first pixel replacement unit 303, a first closing processing unit 304, and a first character pixel determination. 305, a second pixel replacement unit 306, a second closing processing unit 307, a second character pixel determination unit 308, an OR operation unit 309, and the like. With such a configuration, the character pixel determination unit 104 determines character pixels from the transparent image overlapping region 50K based on the frequency distribution of each block 51 calculated by the histogram calculation unit 103 as follows. Hereinafter, each of the 24 comparison operation units is distinguished from “first comparison operation unit 201”, “second comparison operation unit 202”,..., “24th comparison operation unit 224”. May be described.

  There are 24 combinations of two blocks 51 that are adjacent to each other in the transparent image superimposing region 50K in the vertical or front-rear direction. The character pixel determination unit 104 is provided with a comparison operation unit for each of these combinations. Then, the comparison operation unit compares the frequency distributions of the blocks 51 calculated by the histogram calculation unit 103.

  For example, the first comparison operation unit 201 compares the frequency distribution of the block 51A with the frequency distribution of the block 51B. The second comparison operation unit 202 compares the frequency distribution of the block 51B with the frequency distribution of the block 51C. The third comparison operation unit 203 compares the frequency distribution of the block 51C with the frequency distribution of the block 51D.

  The comparison operation unit compares the frequency distributions of the blocks 51 as follows. As described with reference to FIG. 9, the frequency distribution of the block 51 has two or three peaks. The comparison operation unit compares the peaks of the two blocks 51 having the same brightness.

  For example, the first comparison operation unit 201 compares the frequency of the first brightness Br1 of the block 51A with the frequency of the first brightness Br1 of the block 51B. The frequency of the second brightness Br2 of the block 51A is compared with the frequency of the second brightness Br2 of the block 51B. Further, the frequency of the third brightness Br3 of the block 51A is compared with the frequency of the third brightness Br3 of the block 51B.

  Similarly, the second comparison operation unit 202 compares the frequency of the first brightness Br1 of the block 51B with the frequency of the first brightness Br1 of the block 51C. The frequency of the second brightness Br2 of the block 51B is compared with the frequency of the second brightness Br2 of the block 51C. Further, the frequency of the third brightness Br3 of the block 51B is compared with the frequency of the third brightness Br3 of the block 51C.

  Then, the comparison calculation unit notifies the pixel type lightness determination unit 302 that the lightness difference between the two powers is less than the predetermined value α as a constant lightness, and the difference between the two powers is greater than or equal to the predetermined value α. The brightness is notified as indefinite brightness.

  For example, the frequency distribution of the block 51A is as shown in the histogram of FIG. 9A, and the frequency distribution of the block 51B is as shown in the histogram of FIG. 9B. When both are compared, the frequencies of the pixels of lightness Br3 in both blocks 51 are equal, but the frequencies of the pixels of lightness Br1 are different, and the frequencies of the pixels of lightness Br2 are also different.

  Therefore, the first comparison calculation unit 201 notifies the pixel type lightness determination unit 302 with the lightness Br3 as a constant lightness. Further, the lightness Br1 and the lightness Br2 are notified to the pixel type lightness determination unit 302 as constant lightness or indefinite lightness according to a predetermined value α. For example, if the predetermined value α is “1”, the difference is equal to or greater than “1” if the two frequencies are slightly different. Therefore, the brightness Br1 and the brightness Br2 are notified as indefinite brightness.

  The pixel type lightness determination unit 302 is notified of a total of about 24 constant lightness and about 48 indefinite lightness from the 24 comparison calculation units.

  As described above, the colors of the pixels with density in the transparent image 50a are all the same, the colors of the pixels of the characters in the background image 50b are all the same, and the colors of the background pixels of the characters in the background image 50b are all the same. . Therefore, these constant brightness and indefinite brightness match any one of the first brightness Br1, the second brightness Br2, and the third brightness Br3.

  The pixel type lightness determination unit 302 classifies the notified about 24 constant lightness values by value. In this example, the lightness can be classified into one of the first lightness Br1, the second lightness Br2, and the third lightness Br3. Then, it is determined that the constant lightness classified most is the lightness of the pixels with density of the transmission image 50a. As a result, in this example, the constant lightness classified as the third lightness Br3 is the largest, and it should be determined that the third lightness Br3 is the lightness of the pixels with density in the transmission image 50a. This is because the lightness distribution of the pixels with density in the transmission image 50 a is almost constant in all the blocks 51. Hereinafter, the lightness (constant lightness) determined to be the lightness of the pixels with density in the transmission image 50a is referred to as “density lightness Bn”.

  Further, the pixel type lightness determination unit 302 similarly classifies the notified about 48 indefinite lightness values by value. In this example, it should be classified into one of the first brightness Br1, the second brightness Br2, and the third brightness Br3. Then, the classified indefinite brightness is determined as the brightness of the pixel of the background image 50b if it is not the brightness of the pixel with density of the transmission image 50a. In this example, since the third brightness Br3 is first determined as the brightness of the pixels with density in the transmission image 50a, the first brightness Br1 and the second brightness Br2 are determined by the brightness of the pixels of the background image 50b. If there is, it is determined. Hereinafter, the two brightness values (undefined brightness values) determined to be the brightness values of the pixels of the background image 50b are referred to as “first background image brightness Bh1” and “second background image brightness Bh2,” respectively. Hereinafter, a case where the first brightness Br1 is the first background image brightness Bh1 and the second brightness Br2 is the second background image brightness Bh2 will be described as an example.

  Then, the pixel type lightness determination unit 302 sends the density pixel lightness Bn, the first background image lightness Bh1, and the second background image lightness Bh2 to the first pixel replacement unit 303 and the second pixel replacement unit 306. Notice.

  The first pixel replacement unit 303, the first closing processing unit 304, and the first character pixel determination unit 305 are provided with the image data 70, the density pixel brightness Bn, the first background image brightness Bh 1, and the second background Processing is performed based on the image brightness Bh2. Here, the procedure of this processing will be described with reference to FIG.

  The first pixel replacement unit 303 searches the original image 50 for pixels that belong to the transparent image superimposition area 50K and whose brightness is the density-concentrated pixel brightness Bn. Thereby, pixels indicated by hatching in FIG. 11A are found. Then, the first pixel replacement unit 303 replaces the pixels having the density pixel brightness Bn with the pixels whose brightness is the first background image brightness Bh1, as shown in FIG. 11B. Replace with. Hereinafter, the image of the transparent image overlapping region 50K that has been subjected to the replacement process by the first pixel replacement unit 303 is referred to as a “replacement processed image 52A”.

  The first closing processing unit 304 performs the closing process on the replacement processed image 52A by expanding and contracting the pixels (pixels indicated by black) of the second background image brightness Bh2. Thereby, a result as shown in FIG. 11C is obtained. Hereinafter, the replacement processed image 52A that has been subjected to the closing process by the first closing processing unit 304 is referred to as a “closing processed image 52B”.

  The brightness of the pixels constituting the closing processed image 52B is one of the first background image brightness Bh1 and the second background image brightness Bh2.

The first character pixel discriminating unit 305 discriminates the pixel having the larger number of pixels of the first background image brightness Bh1 and the second background image brightness Bh2 as a character pixel. Then, the closing processing image 52B is binarized so that those pixels become “1” and other pixels become “0”. Thereby, a result as shown in FIG. 11D is obtained. In FIG. 11D, the value of a pixel with a black dot is “1”, and the value of a pixel without a black dot is “0”. The same applies to FIG. 12D and FIG.
Hereinafter, the closing image 52B binarized by the first character pixel determination unit 305 is referred to as a “first binary image 52C”.

  The second pixel replacement unit 306, the second closing processing unit 307, and the second character pixel determination unit 308 are also the first pixel replacement unit 303, the first closing processing unit 304, and the first character pixel determination. Similarly to the unit 305, processing is performed based on the image data 70, the density pixel brightness Bn, the first background image brightness Bh1, and the second background image brightness Bh2. However, the usage of the first background image brightness Bh1 and the second background image brightness Bh2 is different.

  Hereinafter, the processes of the second pixel replacement unit 306, the second closing processing unit 307, and the second character pixel determination unit 308 will be described with reference to FIG.

  The second pixel replacement unit 306 searches the original image 50 for pixels that belong to the transparent image superimposition area 50K and whose brightness is the density-concentrated pixel brightness Bn. As shown in FIG. The pixel is replaced with a pixel (pixel shown in black) whose brightness is the second background image brightness Bh2. Hereinafter, the image of the transparent image overlapping region 50K that has been subjected to the replacement process by the second pixel replacement unit 306 is referred to as a “replacement processed image 53A”.

  The second closing processing unit 307 performs a closing process on the replacement-processed image 53A by expanding and contracting the pixels (pixels shown in gray) of the first background image brightness Bh1. As a result, a result as shown in FIG. Hereinafter, the replacement processed image 53A subjected to the closing process by the second closing processing unit 307 is referred to as a “closing processed image 53B”.

  The brightness of the pixels constituting the replacement processed image 53A is one of the first background image brightness Bh1 and the second background image brightness Bh2, similarly to the brightness of the pixels constituting the closing processed image 52B.

  The second character pixel discriminating unit 308 discriminates the pixel having the larger number of pixels of the first background image brightness Bh1 and the second background image brightness Bh2 as a character pixel. Then, the closing processing image 53B is binarized so that those pixels become “1” and other pixels become “0”. As a result, a result as shown in FIG. Hereinafter, the closing processed image 53B binarized by the second character pixel determining unit 308 is referred to as a “second binary image 53C”.

  As shown in FIG. 13, the logical sum operation unit 309 calculates the logical sum of the pixel values of the first binary image 52C and the pixel values of the second binary image 53C at the same position. The binary image 54 represents the logical sum of each position.

  A pixel having a value “1” in the binary image 54 corresponds to a pixel of a character in the transparent image overlapping area 50K.

  As described above, the pixel of the character in the transparent image superimposing region 50K is determined by the processing of each unit of the character pixel determining unit 104.

  Returning to FIG. 3, the transparent image superimposing region correcting unit 105 corrects the transparent image superimposing region 50 </ b> K in the original image 50 based on the determination result by the character pixel determining unit 104. For example, edge enhancement processing is performed on a pixel group determined to be a pixel of a character, and blurring processing is performed on the remaining portion. Hereinafter, the original image 50 processed by the transparent image superimposing region correction unit 105 is referred to as a “corrected image 60”.

  Thereafter, the printing apparatus 10f prints the corrected image 60 on a sheet. Alternatively, the network interface 10g transmits the image data of the corrected image 60 to the personal computer 4A or the like.

  According to the present embodiment, it is possible to detect a character on which a transparent image is superimposed with higher accuracy than in the past.

  In the present embodiment, the transparent image overlapping region 50K is detected based on the regularity of the position of the isolated point that is a pixel with density, but the image data 70 includes data indicating the position of the transparent image 50a in advance. May be detected based on this data.

  In the present embodiment, the transparent image overlapping area 50K is divided into 16 blocks 51, but may be divided into less than 16 blocks 51, or may be divided into 17 or more blocks 51.

  In the present embodiment, the frequency distributions of the blocks 51 that are adjacent vertically and horizontally are compared, but other combinations may be used. For example, the frequency distributions of the blocks 51 arranged in an oblique direction may be compared. Alternatively, only the frequency distributions of the blocks 51 adjacent in the vertical direction may be compared, or only the frequency distributions of the blocks 51 adjacent in the horizontal direction may be compared.

  In the present embodiment, the transparent image overlapping region 50K is equally divided into a plurality of blocks 51, but may be divided into a plurality of blocks 51 having different sizes. In this case, however, it is desirable that the histogram calculation unit 103 in FIG. 3 calculates the ratio of the pixels of each brightness with respect to the entire block 51 instead of the number.

  In the present embodiment, as described with reference to FIGS. 11 to 13, the pixels constituting the character are determined by the closing process and the logical sum operation.

  As described above, among the lightness of the three peaks, the lightness of the pixel with density of the transmission image 50a can be specified first. In the example of FIG. 9, the third brightness Br3 is the brightness of the pixels with density in the transmission image 50a.

  Therefore, one of the remaining two peak lightness values (first lightness Br1 and second lightness Br2) is a pixel constituting a character, and the other is a pixel constituting a character background.

  It may be determined by which method other than the method described with reference to FIGS.

  For example, the sum of the frequencies of the first brightness Br1 of the blocks 51A to 51P is calculated, and the sum of the frequencies of the second brightness Br2 of the blocks 51A to 51P is calculated. Then, the lightness with the smaller sum may be determined as the lightness of the character, and the lightness with the larger sum may be determined as the lightness of the background of the character.

  According to this method, in the example of FIG. 9, it is determined that the second brightness Br2 is the brightness of the character, and the first brightness Br1 is determined to be the brightness of the background of the character.

  In the present embodiment, detection of characters from the transparent image overlapping area 50K is mainly performed by the image processing circuit 10j, but it can also be performed by causing the CPU 10a to execute a computer program. In this case, the program module having the processing routine of the transparent image superimposing region extraction unit 101 to the transparent image superimposing region correction unit 105 shown in FIG. 3 as the main routine and the first comparison operation unit 201 or logical sum operation shown in FIG. What is necessary is just to prepare the computer program which has the program module which makes the procedure of the process of the part 309 a subroutine. The computer program may be stored in the ROM 10c or the mass storage device 10d and executed by the CPU 10a.

  In the present embodiment, the histogram calculation unit 103 calculates the frequency distribution of brightness, but may calculate the frequency distribution of other attributes. For example, hue or saturation may be used instead of lightness. When the original image 50 is a monochrome image, the density frequency distribution may be calculated.

  In addition, the configuration of the entire image forming apparatus 1 or each unit, processing contents, processing order, data configuration, and the like can be appropriately changed in accordance with the spirit of the present invention.

1 Image forming apparatus 10j Image processing circuit (character detection apparatus)
103 Histogram calculation unit (frequency distribution calculation means)
104 Character pixel discrimination unit (character detection means)
303 1st pixel replacement part (replacement image generation means)
304 1st closing process part (1st closing process means)
306 Second pixel replacement unit (replacement image generation means)
307 Second closing processing unit (second closing processing means)
309 OR operation unit (character detection means)
50a Transmission image (second image)
50b Background image (first image)
50K transparent image superimposition area (superimposition area)
51 blocks

Claims (5)

A character detection device for detecting the character from an image including a first image representing a character and a second image representing a translucent object,
A frequency distribution calculating means for calculating the frequency of appearance for each gradation of the attribute of the pixel for each block obtained by dividing the overlapping area, which is an area where the second image overlaps the first image,
Of the frequencies, a first frequency that is a frequency in a first gradation of the gradations, a second frequency that is a frequency in a second gradation of the gradations, and the gradation The third frequency that is the frequency in the third gradation is the peak, and the difference between the third frequencies in each block is the difference between the first frequencies in each block and If the difference between the second frequencies in each block is larger than the difference between the second frequencies, the gradation of the third gradation pixel among the pixels in the overlapping region is changed to the first gradation. A replacement image that generates one replacement image and generates a second replacement image by changing the gradation of the pixel of the third gradation among the pixels of the overlapping region to the second gradation Generating means;
First closing processing means for closing pixels of the second gradation of the first replacement image;
Second closing processing means for closing the first gradation pixel of the second replacement image;
Of the pixels in the overlapping region, the same position as the first gradation pixel of the first replacement image after closing or the second gradation pixel of the second replacement image after closing Character detection means for detecting a set of pixels at the same position as the character;
A character detection device comprising: If the second image is a color image, the attribute is color brightness.
Claim 1 Symbol placement of character detection device. When the first image and the second image are monochrome images, the attribute is density.
Claim 1 Symbol placement of character detection device. A character detection method for detecting the character from an image including a first image representing a character and a second image representing a translucent object,
For each block obtained by dividing the overlap region, which is the region where the second image overlaps the first image, into a plurality of blocks, the frequency of appearance for each gradation of the pixel attribute is calculated,
Of the frequencies, a first frequency that is a frequency in a first gradation of the gradations, a second frequency that is a frequency in a second gradation of the gradations, and the gradation The third frequency that is the frequency in the third gradation is the peak, and the difference between the third frequencies in each block is the difference between the first frequencies in each block and If the difference between the second frequencies in each block is larger than the difference between the second frequencies, the gradation of the third gradation pixel among the pixels in the overlapping region is changed to the first gradation. Generating one replacement image, and generating a second replacement image by changing the gradation of the third gradation pixel of the pixels in the overlap region to the second gradation;
Closing the second gradation pixel of the first replacement image;
Closing the first gradation pixel of the second replacement image;
Of the pixels in the overlapping region, the same position as the first gradation pixel of the first replacement image after closing or the second gradation pixel of the second replacement image after closing A set of pixels at the same position is detected as the character;
The character detection method characterized by the above-mentioned. A computer program used in a computer for detecting a character from an image including a first image representing a character and a second image representing a translucent object,
In the computer,
For each block obtained by dividing the overlapping region, which is the region where the second image overlaps the first image, into a plurality of blocks, a process of calculating the frequency of appearance for each gradation of the pixel attribute is executed,
Of the frequencies, a first frequency that is a frequency in a first gradation of the gradations, a second frequency that is a frequency in a second gradation of the gradations, and the gradation The third frequency that is the frequency in the third gradation is the peak, and the difference between the third frequencies in each block is the difference between the first frequencies in each block and If the difference between the second frequencies in each block is larger than the difference between the second frequencies, the gradation of the third gradation pixel among the pixels in the overlapping region is changed to the first gradation. Generate one replacement image, and execute a process of generating a second replacement image by changing the gradation of the third gradation pixel of the pixels in the overlapping region to the second gradation Let
Executing a process of closing the pixels of the second gradation of the first replacement image;
Executing a process of closing the pixels of the first gradation of the second replacement image;
Of the pixels in the overlapping region, the same position as the first gradation pixel of the first replacement image after closing or the second gradation pixel of the second replacement image after closing A process of detecting a set of pixels at the same position as the character;
A computer program characterized by the above.

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